265 results about "Information needs" patented technology

The invention discloses a method for generating and constructing a database test knowledge base based on an open source large language model and retrieval enhancement, and relates to the technical field of database testing, and the method comprises the steps: building a local deployment system on a local computer system or server, and achieving the efficient local operation of the open source large language model and the stable output of language processing service; on a local computer system or a server, a retrieval enhancement generation framework is constructed, and accurate knowledge support and reliable content output are provided for local language processing service by integrating multi-modal knowledge, optimizing retrieval and generation processes and guaranteeing safety and compliance; on a local computer system or a server, a personalized database knowledge base is built, database related information is collected and processed from multiple channels and is output in an adaptive form, and the specified information requirement of local personalized language processing service is met. According to the method, efficient and stable operation of the open source large language model is realized, accurate and reliable language processing output is realized, and personalized services meet specific requirements.

The invention relates to the technical field of artificial intelligence, in particular to an agent dynamic decision network generation method based on reinforcement learning. The method comprises the following steps: receiving information demand data input by a user; performing feature analysis on the information demand data, and extracting a business target, a constraint condition and a key parameter to obtain an information demand analysis result; identifying a task flow corresponding to the information demand analysis result, and matching an API call chain according to the task flow to obtain a task demand technology blueprint; generating an agent workflow according to the task demand technology blueprint by using a preset dynamic workflow engine; performing context analysis on the language demand analysis result to obtain context information; the agent workflow is divided into ultra-long thinking chains based on context information. In conclusion, through the reinforcement learning technology, the intelligent agent can be automatically generated and continuously optimized according to user requirements, and efficient decision making and dynamic adaptation of complex service scenes are supported.

The invention discloses a multi-agent dynamic arrangement method based on multi-modal analysis and adaptive retrieval, and relates to the technical field of artificial intelligence and information retrieval. Comprising the steps of S1, converting a text, an image, structured data and voice content input by a user into a unified multi-mode semantic representation, S2, converting the unified multi-mode semantic representation into a specific execution process, and S3, automatically scheduling a reasoning agent, a knowledge obtaining agent and an execution agent according to DAG nodes, task elements and available resources, and obtaining the task elements and the execution agent according to the reasoning agent, the knowledge obtaining agent and the execution agent. S4, after task process construction and agent arrangement are completed, dynamic retrieval, evidence convergence and strategy optimization are carried out on information requirements related to a user task, so that a reasoning agent obtains complete knowledge support with consistent context, and S5, knowledge evidence is combined with a task process, so that the task process is completed. The method comprises the following steps: step S6, implementing problem solving, strategy generation and task closed-loop execution through a reasoning agent, step S6, performing actual operation on a target task by an execution agent according to an executable instruction sequence output by the reasoning agent, and outputting a result, and step S7, performing result verification according to an output result returned by the execution agent, and the correctness, integrity and consistency of an output result are examined through rule verification, model evaluation and evidence alignment.

The invention belongs to the technical field of artificial intelligence and multi-modal large model reasoning enhancement, and discloses a multi-modal retrieval enhancement generation method based on gradual group relative strategy optimization. A step-by-step reasoning track construction mechanism is introduced, an original problem is disassembled into a plurality of sub-problems, and in each step, a new retrieval query is autonomously generated in combination with reasoning history and current information requirements, and a most appropriate knowledge source is selected for evidence retrieval; and in the reasoning process, each step of decision and answer obtains a fine-grained reward signal. According to the method, a group relative strategy optimization method is adopted, the query quality of each reasoning step, the knowledge base routing accuracy, the answer content format compliance and the final answer accuracy are used as step-by-step rewards for joint modeling, and model parameters are optimized through global and local multiple feedback signals. The method is remarkably superior to the existing similar technology in tasks such as multi-class multi-modal open domain question answering and complex reasoning, and has excellent answer accuracy, retrieval efficiency and multi-modal adaptive capacity.

The invention provides a digital human video generation method and device based on a large model, an intelligent agent, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical fields of deep learning, the large model, computer vision and the like, and can be applied to scenes such as video live broadcast, advertisement production, e-commerce sales and the like. The digital human video generation method based on the large model comprises the steps that demand information is acquired, the demand information comprises action description information used for describing a specified action video clip, and the action video clip represents a specified action of a target object; processing the demand information by utilizing a language large model to obtain a target script which comprises a target oral playing fragment text matched with the action description information; and processing the target script and the action video clip by using a visual large model to obtain a target video which is used for displaying oral playing of the target object based on the target oral playing clip text in the process of executing the specified action.

The invention relates to the technical field of information processing, and discloses a big data-based information matching management system, which comprises an acquisition module, an attribute analysis module, a multi-dimensional modeling module and a dynamic matching module. The acquisition module acquires a user behavior data set and extracts active information demand features; the attribute analysis module analyzes the real-time updating frequency, the semantic association degree, the historical matching success rate and the data topological structure characteristics of the target information resources; a multi-dimensional modeling module performs heterogeneous modeling on each feature to generate timeliness, semantic association, matching probability feature vectors and topological structure vectors; and the dynamic matching module generates an adaptation degree score and determines an optimal matching object through heterogeneous space projection and feature coupling operation. The system improves the accuracy, real-time performance and efficiency of information matching through multi-dimensional feature processing, topological structure analysis and a dynamic matching algorithm, and is suitable for an accurate information matching scene in a big data environment.

The invention relates to the technical field of information pushing, in particular to a platform user interest recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring user action data and environment perception data, analyzing the user action data and the environment perception data, and constructing a unified space-time semantic network; according to the unified space-time semantic network, analyzing scene features of the user, and determining a multi-dimensional dynamic scene feature set; and analyzing the multi-dimensional dynamic scene feature set, determining composite scene demand information of the current user, and retrieving and pushing real-time demand information of the user according to the composite scene demand information. According to the method and the device, the perception capability of the pushed information to the user demand scene is improved, the multi-dimensional dynamic adaptation of the pushed information to the platform user is realized, the information pushing accuracy for the platform user is improved, and the composite information demand of the user in a complex scene is met.

The embodiment of the invention provides a method, device and system for constructing an attack graph, and a storage medium, and relates to the technical field of network security. The method comprises the steps that based on a test case knowledge base of a target system, an attack graph information expansion algorithm is executed through a knowledge graph technology, and expanded information is obtained; utilizing the expanded information to construct an attribute attack graph of the target system; and carrying out attack path description on vulnerabilities of the target system based on the attribute attack graph. An attack graph information expansion algorithm is executed through a test case knowledge base based on a target system and by means of a knowledge graph technology, information needed for constructing an attack graph can be obtained, potential attack paths can be mined, the attack graph information is expanded, and then a comprehensive and accurate attribute attack graph is constructed. According to the method, effective description of the vulnerability attack path of the target system is realized, so that the constructed attack graph can reflect the security condition of the network system more truly, more scientific decision support is provided for network security management personnel, and the network security protection effect is improved.

The present disclosure generally relates to generating and / or modifying text using a digital assistant. This disclosure encompasses a system for requesting information before generating text using models based on the type of information needed. This disclosure further encompasses a system for a remote model requesting additional information before generating text. This disclosure encompasses user interfaces for text generation / modification. This disclosure encompasses a system for proofreading text using a model. This disclosure encompasses a system for generating a response to a received communication with operations for updating the generated response.

The present disclosure generally relates to generating and / or modifying text using a digital assistant. This disclosure encompasses a system for requesting information before generating text using models based on the type of information needed. This disclosure further encompasses a system for a remote model requesting additional information before generating text. This disclosure encompasses user interfaces for text generation / modification. This disclosure encompasses a system for proofreading text using a model. This disclosure encompasses a system for generating a response to a received communication with operations for updating the generated response.

The invention provides an outline content generation method and system combined with natural language processing, and the method comprises the steps: firstly carrying out the medical semantic analysis of a medical question text inputted by a user, obtaining a question feature set, calling a medical literature database interface for retrieval based on the question feature set, and obtaining a matched medical literature knowledge set; extracting medical knowledge units with independent semantics from the literature knowledge set to generate a structured knowledge unit set, performing association matching on the structured knowledge unit set and the question feature set, distributing weights according to question type identifiers and information demand strength parameters to generate a weighted knowledge integration result, and performing knowledge integration on the weighted knowledge integration result; and finally, generating a medical question answering text according to a weighted knowledge integration result, and feeding back the medical question answering text to a user interaction interface for display, so that accurate, comprehensive and personalized medical question answering can be provided for the user, and the question answering quality and the user experience of a medical academic community are improved.

The invention provides a scientific and technical literature question and answer method and system based on self-feedback iterative optimization, and the method comprises the steps: receiving a user question, and generating an initial response through employing a retrieval enhancement generation strategy; taking the initial response as iterative input, and performing multi-dimensional evaluation on the iterative input to obtain structured feedback with a plurality of specific feedbacks; extracting an information demand from the structured feedback, and performing supplementary retrieval according to the information demand to obtain supplementary information; performing progressive information fusion on the supplementary information and the iterative input according to the priority ranking of the specific feedback to obtain an optimized content; verifying and correcting references in the optimized contents to obtain iterative output; and judging whether an iteration termination condition is met or not according to the iteration output, if so, taking the iteration output as a final response, and otherwise, taking the iteration output input as the iteration input to carry out iteration optimization until the final response is obtained. According to the invention, the answering quality of academic answers can be greatly improved.

The invention discloses a traffic knowledge graph automatic generation system and method, relates to the technical field of traffic informatization and smart travel, and aims to solve the problem that an existing traffic information management system is difficult to effectively classify and integrate multi-source heterogeneous data. The system comprises a collection device used for collecting multi-source heterogeneous data in the traffic field and sending the multi-source heterogeneous data to a traffic knowledge graph generation module; the traffic knowledge graph generation module is used for generating and managing a traffic knowledge graph based on the multi-source heterogeneous data and sending the traffic knowledge graph to the server for storage; and the terminal equipment is used for sending an interaction instruction to the traffic knowledge graph generation module and receiving target information generated by the traffic knowledge graph generation module based on the interaction instruction. The traffic knowledge graph automatic generation system provided by the invention is used for carrying out efficient integration and classification on multi-source heterogeneous data so as to meet information requirements of different scenes in a complex traffic environment.

A system and method for an AI-based Automated Software Application Development Platform which intelligently gathers requirements from a business analyst and generates a runnable software application from those requirements. The AI-based platform enables the business analyst with no technical knowledge to visually model their requirements rapidly without requiring any programming Using the abstract requirements architecture and the platform, the analyst creates requirements models for the application's business processes and business tasks for each of the processes, including defining the supporting information needs. The system intelligently assists the user in defining the information needs for each of the business processes and their tasks. Once the requirements models are created by the analyst with the help of the platform, the platform automatically designs and creates an appropriate persistent canonical data model for the application from the defined information needs. The platform then creates a runnable software application. The system also provides a template for the user to input their requirements in descriptive text, and accepts requirements document for applications as written free form text or text documents written in English as input, and in a non-English language that the system automatically translates them into English. The system provides a method for reverse engineering an existing application, including generating a requirements document for the existing application such as a legacy modernization or digital transformation an old applications for redesigning, updating or revising them to accommodate a new requirements set for such applications.

The embodiment of the invention provides a memory information processing method of a large language model and a computer program product, and relates to the technical field of large language models. The method comprises the steps of obtaining interaction request information of a target object, extracting at least one piece of memory information needing to be memorized from the interaction request information based on a large language model, performing classification and importance evaluation on each piece of memory information to obtain category information and an importance score of each piece of memory information, and according to each piece of category information, determining the importance score of each piece of memory information. And determining the expiration duration of each piece of memory information, and storing each piece of memory information, the corresponding category information, the corresponding expiration duration and the corresponding importance score into an active memory pool of the target object. Context management can be carried out by using the memory information, and the context management accuracy of a large language model in a cross-session scene is remarkably improved.

The present disclosure generally relates to generating and / or modifying text using a digital assistant. This disclosure encompasses a system for requesting information before generating text using models based on the type of information needed. This disclosure further encompasses a system for a remote model requesting additional information before generating text. This disclosure encompasses user interfaces for text generation / modification. This disclosure encompasses a system for proofreading text using a model. This disclosure encompasses a system for generating a response to a received communication with operations for updating the generated response.

Disclosed are a service demand-driven routing scheduling and dynamic selection method and system for active distribution network. The method includes: evaluating a physical importance index and an information importance index of each service traffic according to a physical influence and an information demand of each issued service traffic, comprehensively taking into account the physical importance index and the information importance index to obtain a comprehensive importance index, and ranking the comprehensive importance index to obtain a ranked service traffic set; quantizing the real-time performance and the reliability of a route link and generating a route distance matrix; inputting the ranked service traffic set and the route distance matrix into a route scheduling optimization model, and generating a route scheduling scheme of the overall service traffic based on a principle of service traffic importance and route path performance matching.

The invention discloses a configuration information pushing method, electronic equipment and a storage medium, and the method comprises the steps: obtaining first configuration information of a local configuration file for at least one configuration item of a target micro-service, and obtaining second configuration information of a configuration center for at least one configuration item of the target micro-service in response to the updating of the local configuration file; comparing the first configuration information with the second configuration information to determine difference information of each configuration item; and pushing each piece of difference information to a configuration center to complete configuration pushing processing. According to the method and the device, the configuration information needing to be pushed, namely the difference information, is determined by comparing the configuration information of the local configuration file and the configuration information of the configuration center, and then the difference information of each configuration item is pushed to the configuration center, so that accurate pushing of each configuration item is completed, configuration pushing of the target micro-service in various scenes is met, the cost is reduced, and the user experience is improved. And the stable operation of the application program is correspondingly improved.

The invention belongs to the technical field of clause analysis, and particularly relates to an insurance clause analysis system based on an AI large model, which comprises an input module, a processor, a network connection end, an analysis submission end and display equipment, and is characterized in that the processor is integrated with an AI technology; the output end of the input module is connected with the processor, the output end of the processor is connected with the display device, the processor is in communication connection with the network connection end, and the analysis submission end is in communication connection with the network connection end. According to the design of the scheme, based on the clamping of the AI technology, insurance terms are usually long in length and complex in content, and manual analysis needs to consume a large amount of time and energy. The AI can process a large number of insurance clause texts in a short time, quickly extract key information, and greatly improve the analysis speed. When a customer or an insurance practitioner needs to know the insurance clause information, the AI system can carry out analysis and feedback in real time, and does not need to queue up to wait for processing like manual work, so that the information requirement of the user can be met in time.

An entangled links mechanism to establish and maintain bipartite temporal intimacy between pairs of computers using an idempotent, reversible token method, which presents no observable external “change” until communication of information needs to occur between the computers and which maintains the potential for “bounded (or unbounded) reversibility” in case the intended information dispatched by a source computational entity is not captured or properly accepted by a destination computational entity. The mechanism enables distributed computers in a network to remain continuously aware of each other's presence; to communicate on a logically nearest neighbor basis in a secure and reliable manner in which packets passed over these links do not conflict with normal traffic or cause the available resources of the link to exceeded; and that atomicity, isolation, and “reversible durability” may be maintained for transactions when perturbations occur.

The invention discloses a multi-Agent cooperation AADL model AGREE verification statement automatic generation and verification method. The method comprises the following steps: S1, constructing a domain knowledge base; s2, the Agent is analyzed by the AADL model to extract key information; s3, the demand analysis Agent splits atomic propositions from the natural language demand; s4, the AGREE generator Agent generates an AGREE attachment code; s5, the model fusion Agent automatically embeds the AGREE specification into the target AADL component; s6, the AADL verification Agent performs iterative verification and automatic repair, and a multi-dimensional verification system is constructed; and S7, executing AGREE-based AADL architecture formalized verification, and performing logic attribute verification on the grammar compliance model. According to the invention, innovative fusion of a formalized verification method and an intelligent technology is realized, and the core problem that formalized verification of the AADL architecture is difficult to implement is effectively solved.

The invention provides a full-life-cycle management method for a project, and the method comprises the steps: S1, obtaining a project material, and displaying the project material on a first operation interface; s2, generating a project model on the second operation interface according to the response information of the first operation interface in the step S1; the response information is an approval result of an approval department on project materials, and the project model is a building information model of the project; s3, according to the project model of the second operation interface, generating a construction process specification of the project in a third operation interface, and comparing the construction process specification with an actual construction video of the project; if the actual construction video is consistent with the construction process specification, not giving an alarm; and if the actual construction video is inconsistent with the construction process specification, giving an alarm. Through the mode, systematic management is carried out on the whole life cycle of the project, information needed in the process is integrated, and the problems of information lag or repeated approval and the like possibly occurring when all stages of the engineering project are advanced are avoided.

The invention discloses an intelligent short message anti-interception method and device based on AI deep learning, and a storage medium, and the method comprises the steps: setting a short message sending demand obtaining module which is used for obtaining the short message content which needs to be sent by each client; an information classification module is arranged to detect and automatically classify short message content information needing to be sent; an intelligent short message reconstruction module is arranged, and an intelligent short message and a short message sending scheme are regenerated by using short message content information provided by a client; a short message interception simulation module is arranged and used for simulating short message interception and evaluating a short message sending success rate and a click rate; when the sending success rate is lower than a threshold value, the short message interception simulation module feeds back a reason possibly causing interception to the intelligent short message generation module, and the intelligent short message generation module improves short message content in a targeted manner and regenerates an intelligent short message and a short message sending scheme until the sending success rate is evaluated to be higher than the threshold value; a user confirmation module is arranged, and when the sending success rate is higher than a threshold value, the intelligent short message and a sending scheme thereof can be fed back to a client.

The invention provides an information deep mining method and system based on a multi-agent cooperation framework, and the method comprises the steps: firstly receiving a to-be-mined information demand, carrying out the task decomposition and cooperative scheduling through a plan expert agent, generating a cooperative task scheduling scheme, and then carrying out the multi-source information cooperative search through a search expert agent according to the scheme, the method comprises the following steps: acquiring multi-source original information, reading an expert agent to carry out deep analysis and association extraction on the multi-source original information, generating structured association information, analyzing the expert agent to carry out multi-level deep reasoning on the structured association information to obtain a deep mining intermediate result, and finally, integrating multi-dimensional verification and fusion on the intermediate result by the expert agent to obtain a deep mining result. And generating a final information deep mining result meeting the requirement, thereby realizing automation, intelligence and high efficiency of information mining, and mining the potential value of the information.

The invention discloses a commercial information desensitization method and system based on spreadsheet files. The system is implemented by a spreadsheet file containing a runnable macro code (VBA). A user inputs business information needing to be desensitized into a pre-named worksheet of a spreadsheet file with a preset system code, and the system identifies and classifies sensitive information in the worksheet and provides a classification result composition for the user to review and edit. And the user completes the editing and reviewing result, namely, the content comparison table before and after desensitization is reserved for future reference. The table is also used for desensitization reference of the whole content of the file. A user operates the system through a button / icon of a VBA form control or a button / icon of a VBA ActiveX control and the content input into a pre-named worksheet (WorkSheets) in a spreadsheet file, and the system is operated by the user through the button / icon of the VBA form control or the button / icon of the VBA ActiveX control and the content input into the pre-named worksheet in the spreadsheet file. According to the invention, low-cost and high-security business information desensitization is realized, and the risk of information leakage in a network transmission process is avoided.

The application relates to the technical fields of artificial intelligence and man-machine natural language dialogue, and provides a general memory management method and system based on a language model, which extracts memory information needing to be memorized from dialogue content with the language model, generates temporary memory, integrates the temporary memory to obtain persistent memory about a dialogue user, and is integrated into a dialogue process of the dialogue user and the language model to form memory of the dialogue user, so that the dialogue of the dialogue system is controlled, memory type classification management is performed according to the importance of the memory information in the persistent memory, so that the persistent memory contains short-term memory and long-term memory, the importance of the memory information in the persistent memory is updated and managed, and the short-term memory and the long-term memory are forgotten and upgraded, so that flexible and efficient storage and calling of user memory independent of the model type are realized, and the humanized, continuous and personalized dialogue experience in the man-machine dialogue process is improved.

The invention belongs to the technical field of code generation, particularly provides a large model tool calling method and system based on demand awareness, and aims to solve the technical problems that an existing code generation system lacks an active tool calling capability and cannot automatically select a proper tool according to information loss. According to the method, an information demand identification model, a tool mapping knowledge graph and a self-adaptive scheduling strategy are constructed, so that a large language model can actively identify an information gap in the code analysis and generation process, and an external tool is automatically selected and called to obtain required context information. The core of the system comprises four modules, namely a demand awareness engine, a tool selection decision maker, a multi-modal information fusion device and an iterative optimization feedback mechanism. By introducing an information missing detection algorithm based on an attention mechanism and a hierarchical tool calling planning strategy, the system can realize intelligent processing from a single file to a warehouse-level code task, and the accuracy and availability of code generation are remarkably improved.

A system and method for building queries using generative artificial intelligence (AI) is described. In an example implementation, the system may include an observability platform configured to receive a user request to use generative artificial intelligence in association with a query on an observability platform. A blob of text may be generated based on the request and include information needed to execute a valid query on the observability platform. A server may be configured to include a generative AI engine and a web service to respond to the blob of text. The observability platform may determine whether the response from the generative AI engine is a valid query, meeting a threshold quality level. User feedback may be requested from the user to determine whether the query results answered the user input for query assistance.

Computer-based information security techniques are provided that enable a user to reduce and control the amount of personal information digitally supplied to any given service provider when transacting or otherwise interacting with these service providers. Using these techniques, the user can release the minimally required personal information needed for a particular service provider to perform a particular service, without a link to a public identity of the user. Stated differently, only the information that a particular service provider needs in order to complete a service provided by the service provider is conveyed to the service provider based on the user's policy settings. An encryption scheme is used to securely transfer the various portions of personal information to the various service providers.

The invention discloses a digital equipment physical ID full life cycle management system based on deep learning, and relates to the technical field of automatic control and intelligent equipment management, and the system comprises the following steps: collecting equipment operation state and physical attribute data through a multi-channel mode, carrying out the preprocessing of the equipment operation state and physical attribute data, extracting multi-level semantic information through a feature fusion method, and carrying out the recognition of the multi-level semantic information; the equipment operation state is recognized through a pre-training model, anomaly detection is carried out, a system combines equipment attribute information and an anomaly result, the corresponding relation between equipment and the digital identity of the equipment is constructed in a hash mode, an equipment digital identity label capable of being dynamically updated and verified is generated, and in the equipment operation process, the system continuously monitors the state change of the equipment, so that the equipment is identified. Whether the identity information needs to be updated or not is automatically judged, recording and verification are completed, and therefore dynamic management and whole-process tracing of the identity of the equipment are achieved. The method supports high-precision identification and flexible expansion, and is suitable for intelligent supervision scenes of various digital industrial devices.